Exploring Key Factors for Long-Term Vessel Incident Risk Prediction
CoRR(2024)
摘要
Factor analysis acts a pivotal role in enhancing maritime safety. Most
previous studies conduct factor analysis within the framework of
incident-related label prediction, where the developed models can be
categorized into short-term and long-term prediction models. The long-term
models offer a more strategic approach, enabling more proactive risk
management, compared to the short-term ones. Nevertheless, few studies have
devoted to rigorously identifying the key factors for the long-term prediction
and undertaking comprehensive factor analysis. Hence, this study aims to delve
into the key factors for predicting the incident risk levels in the subsequent
year given a specific datestamp. The majority of candidate factors potentially
contributing to the incident risk are collected from vessels' historical safety
performance data spanning up to five years. An improved embedded feature
selection, which integrates Random Forest classifier with a feature filtering
process is proposed to identify key risk-contributing factors from the
candidate pool. The results demonstrate superior performance of the proposed
method in incident prediction and factor interpretability. Comprehensive
analysis is conducted upon the key factors, which could help maritime
stakeholders formulate management strategies for incident prevenion.
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